Interactive Visualization with Plotly - Plotly Express - 1

One should look for what is and not what he thinks should be. (Albert Einstein)

Plotly Express: Topic introduction

In this part of the course, we will cover the following concepts:

  • Introduction to plotly express
  • Organize and visualize data with plotly express

Warm up

  • Check out this interactive graphic from The Guardian about dog breeds and how they are related to each other

  • What surprised you?

  • What did you already know?

Module completion checklist

Objective Complete
Define Plotly Express
Describe univariate plots in Plotly Express

What is plotly?

  • Plotly is a powerful graphing library that is used to create interactive, publication-quality graphs

  • In plotly, you can create line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts

  • Click here for more about plotly

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What is different about plotly express?

  • plotly express contains functions that can create entire figures at once
  • It can be referred as plotly express or PX
  • plotly express is a built-in part of the plotly library which is the recommended for creating most common figures
  • Using the plotly express we can create a majority of the most commonly-used interactive visualizations in data science with single line of code
  • With plotly express, you can make professional interactive visualizations easily and quickly

Module completion checklist

Objective Complete
Define Plotly Express

✔

Describe univariate plots in Plotly Express

Load the dataset and libraries

  • In this course, we will be using the inbuilt tips dataset that’s available in plotly
# Load the libraries and the dataset 
import plotly.express as px

# Load the dataset
tips_dataset = px.data.tips()  
# Top 5 entires of the dataset
tips_dataset.head()
   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

Describe univariate plots in plotly express

  • A univariate plot shows the ins and outs of a single variable

  • There are different type of univariate plots available in plotly express:

    • Histograms
    • Box plots
    • Strip plots
    • Violin plots
    • ECDF plots

  • We will talk about a subset of these univarite plots

Histograms

  • A histogram is a very easy way to visualize the distribution of a single variable. However, with interactive histograms we are not limited to a single variable
  • plotly express allows us to create layered histograms, where layers can be toggled
  • plotly express also has built-in datasets for use with plotting tools, which we will be using in next slides to create visualization
# Create a histogram using plotly function `histogram`
fig = px.histogram(tips_dataset,                  #<- set dataset
          x="total_bill",                         #<- set x variable 
          color="day",                            #<- set grouping variable
          hover_data = tips_dataset.columns)      #<- add column names 
fig.show()

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Histograms (cont’d)

  • While that is a gorgeous plot, we might want to make some changes to make it look more professional

  • Let’s create the plot to measure bill on a daily basis and add the labels, the color scheme and the number of bins

fig = px.histogram(tips_dataset,                   
          x="total_bill",                          
          color="day",                             
          hover_data = tips_dataset.columns,       
          title='Bill by Day',                     
          labels={'total_bill': 'Total Bill'}, 
          nbins=50,                                
          color_discrete_sequence = px.colors.sequential.Rainbow_r)  
fig.show()

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Histograms (cont’d)

  • Try the following:

    • toggling the different color boxes in the legend
    • hovering over a bin, drawing a box with your mouse on the plot
    • clicking the reset axes button
fig = px.histogram(tips_dataset,                   
          x="total_bill",                          
          color="day",                             
          hover_data = tips_dataset.columns,       
          title='Bill by Day',                     
          labels={'total_bill': 'Total Bill'}, 
          nbins=50,                                
          color_discrete_sequence = px.colors.sequential.Rainbow_r)  
fig.show()

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Histograms (cont’d)

  • Let’s create histogram for gender wise total bill and try different plotly colors to build histograms
  • For more info on colors in plotly, click here
fig = px.histogram(tips_dataset,                
          x="total_bill", 
          color="sex", 
          hover_data = tips_dataset.columns, 
          title='Bill by Gender', 
          labels={'total_bill': 'Total Bill'}, 
          nbins=50, 
          color_discrete_sequence = px.colors.sequential.Purp)
fig.show()

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Box plots

  • With interactive box plots, each box plot can be toggled on or off, so all our variables can be visible at once

  • We can choose to set the toggle using the color attribute

  • In this case, we set the color attribute to day column so we can toggle to analyze the total bill across various days

# Construct a box plot using plotly function `box`
fig = px.box(tips_dataset, 
          y="total_bill", 
          color="day")
fig.show()

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Box plots (cont’d)

  • This set of box plots is lovely, but let’s increase the level of visual interest
  • What if we changed the color sequence, the title, the labels, and added an extra grouping variable?
  • While you can toggle the color variable, you can’t toggle the x variable, so be sure which grouping you want to be able to toggle
fig = px.box(tips_dataset, 
          x='sex', 
          y='total_bill', 
          color='day', 
          labels={'total_bill':'Total Bill', 'sex':'Sex'}, 
          title='Total Bill grouped by Day and Sex',
          color_discrete_sequence = px.colors.sequential.Electric)
fig.show()

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Marginal plots

  • Marginal plots are small subplots above or to the right of a main plot, use the argument marginal to create small box plot above the main histogram
# Construct marginal plot by providing marginal argument 
fig = px.histogram(tips_dataset, x="total_bill", 
              color="sex", 
              marginal="box", 
              hover_data = tips_dataset.columns, 
              title='Bill by Gender', 
              labels={'total_bill': 'Total Bill'}, 
              nbins=50)
fig.show()

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Knowledge check

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Module completion checklist

Objective Complete
Define Plotly Express

✔

Describe univariate plots in Plotly Express

✔

Congratulations on completing this module!


You are now ready to try Tasks 1-3 in the Exercise for this topic

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